Speaker identification using minimum classification error training

In this paper we use a Minimum Classification Error (MCE) training paradigm to build a speaker identification system. The training is optimized at the string level for a text-dependent speaker identification task. Experiments performed on a small set speaker identification task show that MCE training can reduce closed-set identification errors by up to 20-25% over a baseline system trained using Maximum Likelihood Estimation. Further experiments suggest that additional improvement can be obtained by using some additional training data from speakers outside the set of registered speakers, leading to an overall reduction of the closed-set identification errors by about 35%.